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Analysis of non-uniform hexagonal cross-sections for thin-walled functionally graded beams using artificial neural networks

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In this study, 1000 data sets randomly generated by the FEM through iterations are used for the training process to get optimal weights. Based on these obtained optimal weights, beam behaviors under the changes in material distribution through thickness could then be predicted. In this model, the ANN’s inputs are the gradation index of the power-law distribution and thickness, while the outputs are compliance and beam displacements. The computed results are verified against those derived from the FEM. |

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